Domain expertise has always been the real moat

Good comment but I think the timelines are not clear. Humans are an algorithm. This was true before AI. In certain domains (playing chess e.g.) machines have already surpassed humans. Humans still play chess though and chess is more popular than ever.

I still drive my car and self driving cars have yet to displace human drivers. I think the sentiment on HN and other places when Google started talking self driving cars circa 2009 is that it's harder than it looks. Typically the first 80% of progress is easy and the rest isn't as easy. We're almost 20 years after a "pretty good self driving car" and we're still not at "self driving cars outperform humans under all situations".

Today humans use AI. You can't fire up Claude and ask it "what do you want to work on today". The amount of context we have as humans is vastly larger than the context LLMs have. If you give LLMs vague context they're completely lost. They are mind blowing in many ways but they are not anywhere close to AGI. They're also not close to being able to build complex software only guided by someone who has no idea what software is and how computers work. They can do some of that but I've yet to see any major successful piece of software built that way. They also consume vast physical resources to get the job done.

Before LLMs I think it was a given that at some point we would have AGI that's smarter than us. Machines we build aren't constrained by the biological constraints we are subject to and can evolve faster than we have. But when that's gonna happen, whether that's actually LLM-like in architecture, and what things will look like once that happens, are fairly open questions at this point. In the mean time LLMs can certainly generate a lot of code and we can use them to build more stuff.

Maybe, but that would require domain experts and stakeholders to write clear specs, and that's never gonna happen in my experience.

I am still bothered that domain experts still keep confusing closing orders with generating a delivery note, or stopping to say articles when they mean a product or a product when they mean an item.

Writing good specs require lots of domain knowledge but a very engineeristic approach these people just don't have.

I think a charitable interpretation is that from the perspective of AI, some domains are shallow (like chess), and some are deep (you can fill in the blank here).

I don't believe this.

I work in e-commerce and warehouse management.

We have put lots of effort at documenting the domain, creating precise unambiguous language, glossaries, E2Es written as user stories etc, etc.

And still models are simply not able to translate Jira tasks to clear specs, even for this well understood and common use case.

Also, they don't understand how changes in one part of the business domain will impact other parts. They can get it right 9 times out of 10, but even that is too little and compounds to deeply wrong implementations.

And they don't understand or know the people involved in these processes and what they REALLY care for or what the real priorities are. Very often political.

And that's not even mentioning the code, that ends up with the lack of proper abstractions or harness.

Or the lack of push back against bad ideas at business or code level.

what does actually playing chess have to do with writing an efficient game tree search algorithm beyond a few simple principles? You challenged him to a programming contest and won, as the vastly more experienced programmer. Even though he could use AI, your domain knowledge here proved to be the deciding factor.